Computational Complexity of Schema-Guided Document Extraction (www.runpulse.com)

Recent research from Pulse highlights significant challenges in schema-guided document extraction, revealing that while defining a JSON schema and using large language models (LLMs) seems straightforward, complexities arise with real-world documents. Initial tests showed that enforcing structured outputs was computationally intensive and could lead to extraction quality degradation, especially with documents featuring nested schemas, optional fields, and variable-length arrays. The study discovered that stricter constraints, while beneficial for cleaner data parsing, often hurt accuracy during extraction, creating a paradox in model performance. The team proposes innovative strategies to tackle these problems, focusing on schema complexity analysis to predict extraction difficulty, adaptive constraint strategies that vary based on document type, and grammar compilation optimization to reuse common substructures efficiently. Additionally, the use of confidence-aware extraction aims to flag low-probability extractions for review. By addressing these intricate challenges at the intersection of formal language theory and LLM optimization, Pulse aims to enhance the reliability of structured data extraction from complex business documents, a crucial need in today’s data-driven landscape.